Geographers used to be interested in diffusion
Hagerstrand et al. (1968)
Passed the torch to economists and sociologists
Why? Lack of granular data:
Because new digital activities are rarely—if ever—captured in official state data, researchers must rely on information gathered from alternative sources (Zook and McCanless 2022).
Understand how the adoption of new technologies evolves
Guide policies for deployment of new technologies
Predictions of introduction times for future technologies (Meade and Islam 2021):
Network operators
Suppliers of network equipment
Regulatory authorities
As in temporal diffusion models, an S-shaped pattern in the cumulative level of adoption
A hierarchy effect: from main centres to secondary ones – central places
A neighborhood effect: diffusion proceeds outwards from innovation centres, first “hitting” nearby rather than far-away locations (Grubler 1990)
Hägerstrand (1965): from innovative centres (core) through a hierarchy of sub-centres, to the periphery
Archived web data
Observe commercial websites 1996 - 2012
Geolocate to a unique location
Geolocate to multiple locations
JISC UK Web Domain Dataset: all archived webpages from the .uk domain 1996-2012
Curated by the British Library
Tranos, E., and C. Stich. 2020. Individual internet usage and the availability of online content of local interest: A multilevel approach. Computers, Environment and Urban Systems, 79:101371.
Tranos, E., T. Kitsos, and R. Ortega-Argilés. 2021. Digital economy in the UK: Regional productivity effects of early adoption. Regional Studies, 55:12, 1924-1938.
Stich, C., E. Tranos and M. Nathan. 2022. Modelling clusters from the ground up: a web data approach. Environment and Planning B, in press.
Tranos, E., A. C. Incera and G. Willis. 2022. Using the web to predict regional trade flows: data extraction, modelling, and validation, Annals of the AAG, in press.
All .uk archived webpages which contain a UK postcode in the web text
Circa 0.5 billion URLs with valid UK postcodes
20080509162138/http://www.website1.co.uk/contact_us IG8 8HD
All the archived .uk webpages
Archived during 1996-2012
Commercial webpages (.co.uk)
From webpages to websites:
- http://www.website1.co.uk/webpage1 and
- http://www.website1.co.uk/webpage2 are part of the
1 vs. multuple postcodes in a website
| level | freq | perc | cumfreq | cumperc |
|---|---|---|---|---|
| (0,1] | 41,596 | 0.718 | 41,596 | 0.718 |
| (1,2] | 6,451 | 0.111 | 48,047 | 0.830 |
| (2,10] | 6,163 | 0.106 | 54,210 | 0.936 |
| (10,100] | 2,975 | 0.051 | 57,185 | 0.988 |
| (100,1000] | 646 | 0.011 | 57,831 | 0.999 |
| (1000,10000] | 62 | 0.001 | 57,893 | 1.000 |
| (10000,100000] | 4 | 0.000 | 57,897 | 1.000 |
Websites with a large number of postcodes: e.g. directories, real estate websites
Focus on websites with one unique postcode per year
Neighborhood effect: diffusion proceeds outwards from innovation centers, first “hitting” nearby rather than far-away locations (Grubler 1990)
Spatial dependency (Moran’s I & LISA maps)
Website density regressions
Websites per firm in Local authorities (c. 400)
Websites in Output Areas (c. 200,000)
\[Website\,Density_{i} = a + \beta Distance\,to\,Place_{i} + e_{i}\]
\(Website\,Density_{i}\):
Websites per firm in a Local Authority \(i\), or
Websites in an Output Area \(i\)
\[Website\,Density_{i} = a + \beta Distance\,to\,Place_{i} + e_{i}\]
\(Place\):
London, or
Nearest city, or
Nearest retail centre
\(\beta\) interpretation:
The lower the \(\beta\) is (or the larger the \(|\beta|\) is)…
… the larger urban gravitation is for web adoption.
Hierarchy effect: from main centers to secondary ones – central places
Spatial dependency relatively small and constant over time / scales
At local scale, consistent hotspots over time
More granular analysis reveals hotspots
Almost perfect polarisation of web adoption in the early stages at a granular level
More equally diffused at the Local Authority level
Plateau overtime
Distance effect: urban gravitation increases over time and then drops
Consistent across scales and definitions of urban
to do: the same for OAs
Geography matters: spatial dependency, urban gravitation
Some indications of a hierarchical diffusion
Granular analysis reveals patterns otherwise not visible
Well-established theoretical approaches of diffusion survive even at a granular level
Explain the spatial patterns of fast/slow web adoption
Applied the same analysis fo OA
Survival regressions (Perkins and Neumayer 2005)
Expand our definition of web to websites with # of postcodes > 1